U2-Net: Going deeper with nested U-structure for salient object detection
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doi.org/10.1016/j.patcog.2020.107404 →Countries where authors are citing U2-Net: Going deeper with nested U-structure for salient object detection
This map shows the geographic impact of U2-Net: Going deeper with nested U-structure for salient object detection. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by U2-Net: Going deeper with nested U-structure for salient object detection with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites U2-Net: Going deeper with nested U-structure for salient object detection more than expected).
Fields of papers citing U2-Net: Going deeper with nested U-structure for salient object detection
This network shows the impact of U2-Net: Going deeper with nested U-structure for salient object detection. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the U2-Net: Going deeper with nested U-structure for salient object detection.
About U2-Net: Going deeper with nested U-structure for salient object detection
This paper, published in 2020, received 1.7k indexed citations . Written by Xuebin Qin, Zichen Zhang, Chenyang Huang, Masood Dehghan, Osmar R. Zai͏̈ane and Martin Jägersand covering the research area of Cognitive Neuroscience and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (887 citations), Artificial Intelligence (266 citations), Media Technology (226 citations), Radiology, Nuclear Medicine and Imaging (190 citations) and Biomedical Engineering (123 citations). Published in Pattern Recognition.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1016/j.patcog.2020.107404.